{"id":"W4404022370","doi":"10.1063/5.0233392","title":"Thermo-economic performance analysis and multi-objective optimization of viscosity ratio and thermal conductivity ratio of copper oxide–palm oil nanolubricants","year":2024,"lang":"en","type":"article","venue":"Physics of Fluids","topic":"Lubricants and Their Additives","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Impact","funders":"Universiti Tenaga Nasional; Ministry of Higher Education, Malaysia","keywords":"Physics; Thermal conductivity; Copper; Viscosity; Palm oil; Copper oxide; Thermodynamics; Oxide; Metallurgy; Food science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001252228,0.0009184591,0.0007868287,0.0008510829,0.00027179,0.0009227291,0.0004594285,0.0006408631,0.000738043],"category_scores_gemma":[0.0009843033,0.0003420494,0.0008988981,0.0005388684,0.00027266,0.0004734179,0.0003370428,0.0005527765,0.0001306645],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007098929,"about_ca_system_score_gemma":0.0005931143,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002038192,"about_ca_topic_score_gemma":0.002765634,"domain_scores_codex":[0.9995963,0.00006865231,0.00002842493,0.00009202986,0.0001463976,0.0000681586],"domain_scores_gemma":[0.9996018,0.0001586613,0.0001167762,0.00002112405,0.00008191098,0.00001975889],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000533902,0.0008282962,0.002869089,0.001043055,0.0001086077,0.000201759,0.00006260451,0.5104918,0.4431201,0.001016175,0.0002358235,0.03948884],"study_design_scores_gemma":[0.00003176548,0.001277279,0.003284947,0.0000303331,0.00008942805,0.00004142459,0.00007380478,0.8153397,0.1789147,0.0002225538,0.0006600155,0.00003401383],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9367776,0.002208085,0.05674942,0.00011099,0.00003090552,0.0001220096,0.0001674808,0.0001045741,0.00372886],"genre_scores_gemma":[0.968245,0.0005892036,0.02962815,0.00002378525,0.000004158549,0.0001300194,0.00009594134,0.00001967514,0.001264137],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002038192,"threshold_uncertainty_score":0.006622553,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01041081618362941,"score_gpt":0.2196344508484614,"score_spread":0.209223634664832,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}